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Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology
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10.1063/1.4803746
/content/aip/journal/jrse/5/3/10.1063/1.4803746
http://aip.metastore.ingenta.com/content/aip/journal/jrse/5/3/10.1063/1.4803746

Figures

Image of FIG. 1.
FIG. 1.

3-dimensional response and 2-dimensional contour plots for hydrogen yield. (a) Xylose vs initial pH. (b) Xylose vs peptone. (c) Initial pH vs peptone. (Hydrogen yield is in mol H/mol xylose.)

Image of FIG. 2.
FIG. 2.

Flowchart of ANN model.

Image of FIG. 3.
FIG. 3.

Schematic representation of topology of ANN model.

Image of FIG. 4.
FIG. 4.

Hydrogen yield correlation predicted by RSM and ANN with the experimental values.

Image of FIG. 5.
FIG. 5.

Plots for optimal topology of neural network. (a) Root mean square error plot for optimal network topology; (b) R plot for optimal network topology.

Tables

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Table I.

Levels of variables and statistical analysis for Plackett-Burman design.

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Table II.

Experiments for path of the steepest ascent.

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Table III.

Central composite design for three independent variables.

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Table IV.

ANOVA results of the experimental response at different factor levels.

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Table V.

Optimized medium composition for hydrogen yield using different methodologies.

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/content/aip/journal/jrse/5/3/10.1063/1.4803746
2013-05-03
2014-04-19
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology
http://aip.metastore.ingenta.com/content/aip/journal/jrse/5/3/10.1063/1.4803746
10.1063/1.4803746
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